Image-guided therapy for cardiovascular applications involves the integration of pre-acquired volumetric imaging data, such as 3D imaging data, e.g. obtained from MRI, CT, Ultrasound, or Fluoroscopic Imaging, with intra-procedural electroanatomical mapping information (EAM), which can be localized by magnetic fields, electrical fields, or ultrasound technology. This strategy is dependent on properly aligning the two datasets, which is a process commonly known as registration.
Existing methods for registration of the EAM and imaging data are based on a point-to-surface distance minimization algorithm (e.g. an iterated closest points algorithm, ICP). This approach requires the specific selection of points on the endocardial surface during the mapping procedure, followed by point-to-surface registration of those EAM points with chamber surface boundaries segmented from the imaging dataset.
The conventional scheme for point acquisition and subsequent point-to-surface registration is skill-dependent, time-consuming, and labor-intensive since the mapping catheter tip must first be manipulated to a landmark on the endocardium and then the tip location must be explicitly annotated within the EAM data record. This process of individual landmark identification and annotation must be repeated between 50-100 times to define the endocardial surface with sufficient detail for ICP-based registration to function accurately. To achieve this, 15-45 minutes of mapping just for the registration procedure may be necessary before the clinical or diagnostic or therapeutic component of the patient study begins. It is important to realize that any acquired EAM point location that is not in contact with the chamber surface adversely impact the quality of registration with point-to-surface distance minimization.
Hence, an improved method for point acquisition would be advantageous allowing for increased flexibility, cost-effectiveness, and timesavings.